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Statistical Analysis using IBM SPSS Statistics

The Statistical Analysis using IBM SPSS Statistics - Essentials training course provides an application-oriented introduction to the statistical component of IBM SPSS Statistics. You will review several statistical techniques and discuss situations in which you would use each technique, how to set up the analysis, as well as how to interpret the results. This includes a broad range of techniques for exploring and summarizing data, as well as investigating and testing underlying relationships. You will gain an understanding of when and why to use these various techniques as well as how to apply them with confidence, interpret their output, and graphically display the results.

The Statistical Analysis using IBM SPSS Statistics - Advanced training course provides an application-oriented introduction to advanced statistical methods available in IBM SPSS Statistics. You will review a variety of advanced statistical techniques and discuss situations in which each technique would be used, the assumptions made by each method, how to set up the analysis, and how to interpret the results. This includes a broad range of techniques for predicting variables, as well as methods to cluster variables and cases.

By attending Statistical Analysis using IBM SPSS Statistics - Essentials workshop, delegates will learn:

  • Introduction to statistical analysis
  • Describing individual variables
  • Testing hypotheses
  • Testing hypotheses on individual variables
  • Testing on the relationship between categorical variables
  • Testing on the difference between two group means
  • Testing on differences between more than two group means
  • Testing on the relationship between scale variables
  • Predicting a scale variable: Regression
  • Introduction to Bayesian statistics
  • Overview of multivariate procedures

By attending Statistical Analysis using IBM SPSS Statistics - Advanced workshop, delegates will learn:

  • Introduction to advanced statistical analysis
  • Grouping variables with Factor Analysis and Principal Components Analysis
  • Grouping cases with Cluster Analysis
  • Predicting categorical targets with Nearest Neighbor Analysis
  • Predicting categorical targets with Discriminant Analysis
  • Predicting categorical targets with Logistic Regression
  • Predicting categorical targets with Decision Trees
  • Introduction to Survival Analysis
  • Introduction to Generalized Linear Models
  • Introduction to Linear Mixed Models

  • Experience with IBM SPSS Statistics

  • This Statistical Analysis using IBM SPSS Statistics - Advanced class is ideal for IBM SPSS Statistics users.

COURSE AGENDA

Statistical Analysis Using IBM SPSS Statistics - Essentials
(Duration : 2 Days)

1

Introduction to statistical analysis

  • Identify the steps in the research process
  • Identify measurement levels
2

Describing individual variables

  • Chart individual variables
  • Summarize individual variables
  • Identify the normal distribution
  • Identify standardized scores
3

Testing hypotheses

  • Principles of statistical testing
  • One-sided versus two-sided testing
  • Type I, type II errors and power
4

Testing hypotheses on individual variables

  • Identify population parameters and sample statistics
  • Examine the distribution of the sample mean
  • Test a hypothesis on the population mean
  • Construct confidence intervals
  • Tests on a single variable
5

Testing on the relationship between categorical variables

  • Chart the relationship
  • Describe the relationship
  • Test the hypothesis of independence
  • Assumptions
  • Identify differences between the groups
  • Measure the strength of the association
6

Testing on the difference between two group means

  • Chart the relationship
  • Describe the relationship
  • Test the hypothesis of two equal group means
  • Assumptions
7

Testing on differences between more than two group means

  • Chart the relationship
  • Describe the relationship
  • Test the hypothesis of all group means being equal
  • Assumptions
  • Identify differences between the group means
8

Testing on the relationship between scale variables

  • Chart the relationship
  • Describe the relationship
  • Test the hypothesis of independence
  • Assumptions
  • Treatment of missing values
9

Predicting a scale variable: Regression

  • Explain linear regression
  • Identify unstandardized and standardized coefficients
  • Assess the fit
  • Examine residuals
  • Include 0-1 independent variables
  • Include categorical independent variables
10

Introduction to Bayesian statistics

  • Bayesian statistics and classical test theory
  • The Bayesian approach
  • Evaluate a null hypothesis
  • Overview of Bayesian procedures in IBM SPSS Statistics
11

Overview of multivariate procedures

  • Overview of supervised models
  • Overview of models to create natural groupings
Statistical Analysis Using IBM SPSS Statistics - Advanced
(Duration : 2 Days)

1

Introduction to advanced statistical analysis

  • Taxonomy of models
  • Overview of supervised models
  • Overview of models to create natural groupings
2

Grouping variables with Factor Analysis and Principal Components Analysis

  • Factor Analysis basics
  • Principal Components basics
  • Assumptions of Factor Analysis
  • Key issues in Factor Analysis
  • Use Factor and component scores
3

Grouping cases with Cluster Analysis

  • Cluster Analysis basics
  • Key issues in Cluster Analysis
  • K-Means Cluster Analysis
  • Assumptions of K-Means Cluster Analysis
  • TwoStep Cluster Analysis
  • Assumptions of TwoStep Cluster Analysis
4

Predicting categorical targets with Nearest Neighbor Analysis

  • Nearest Neighbors Analysis basics
  • Key issues in Nearest Neighbor Analysis
  • Assess model fit
5

Predicting categorical targets with Discriminant Analysis

  • Discriminant Analysis basics
  • The Discriminant Analysis model
  • Assumptions of Discriminant Analysis
  • Validate the solution
6

Predicting categorical targets with Logistic Regression

  • Binary Logistic Regression basics
  • The Binary Logistic Regression model
  • Multinomial Logistic Regression basics
  • Assumptions of Logistic Regression procedures
  • Test hypotheses
  • ROC curves
7

Predicting categorical targets with Decision Trees

  • Decision Trees basics
  • Explore CHAID
  • Explore C&RT
  • Compare Decision Trees methods
8

Introduction to Survival Analysis

  • Survival Analysis basics
  • Kaplan-Meier Analysis
  • Assumptions of Kaplan-Meier Analysis
  • Cox Regression
  • Assumptions of Cox Regression
9

Introduction to Generalized Linear Models

  • Generalized Linear Models basics
  • Available distributions
  • Available link functions
10

Introduction to Linear Mixed Models

  • Linear Mixed Models basics
  • Hierarchical Linear Models
  • Modeling strategy
  • Assumptions of Linear Mixed Models

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